Browsing by Author "Ilekura, Idowu Oselumhe"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- Estimating wave runup using satellite multi-spectral imageryPublication . Ilekura, Idowu Oselumhe; Almeida, Luis Pedro; Ferreira, Óscar ManuelThe wave runup is one of the most important processes responsible for coastal hazards, including overtopping or erosion. Understanding and predicting wave runup in any coastal environment is crucial for risk and vulnerability assessment studies. Nevertheless, the lack of field observations of wave runup is one of the main limitations of the predictability of this process. Past studies have used shore-based video monitoring techniques to observe wave runup in coastal areas. However, these studies were limited in time (data acquisition periods of several months or years) and space (spatial coverage of a single beach or extension of hundreds of meters or a few kilometres). In recent years, remote sensing, in particular Satellite Imagery, have improved the capability of the onboard sensor (e.g., improved spatial resolution of optical sensors) and revisit times (time between consecutive data collection in the same point on the earth’s surface), making of this technology one with the most significant potential to overcome earth sciences challenges. The present project’s general objective is to utilize multi-spectral imagery to monitor wave runup in coastal areas, representing a novel approach compared to past runup monitoring methodologies. Wetsand (boundary between the dry and wet beach) and Waterline (boundary between the water and the beach) were extracted from the satellite images as potential runup proxies. The satellite-derived runup proxies were compared to existing wave runup formulations. The error quantification was performed using statistical descriptive parameters (e.g., RMSE, correlation coefficient, and Bias). The waterline-derived runup proxies demonstrated high correlation (Bias = 0.35, R² = 0.63, RMSE = 0.65) with the existing runup formulation, whereas the Wetsand proxies exhibited lower correlation (Bias = -0.41, R² = 0.17, RMSE = 0.95). Averaging the Wetsand and Waterline proxies improved the Bias and RMSE to 0.12 and 0.611, respectively. The optimal formulation for each proxy was employed to correct the runup formulation, which was then used to compute the 𝑅2, resulting in a refined runup formulation. The corrected formulations for each proxy were utilized to predict extreme runup events. The waterline and the average Wetsand/Waterline proxies outperformed the Wetsand proxies during low wave and tide conditions. In contrast, the Wetsand proxy outperformed both alternatives in predicting extreme runup under high wave and tide conditions. Overall, the study noted the prospect of using satellites to measure and estimate runup globally.